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Added FamScore function, added remove_edges function in network graph

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master
Luca Moretti 4 years ago
parent 0112859665
commit 75c689ff42
4 changed files with 177 additions and 54 deletions
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  1. 182
      main_package/classes/fam_score_calculator.py
  2. 11
      main_package/classes/network_graph.py
  3. 23
      main_package/classes/structure_score_based_estimator.py
  4. 1
      main_package/tests/test_structure_score_based_estimator.py

182
main_package/classes/fam_score_calculator.py
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@ -9,7 +9,7 @@ from networkx.readwrite import json_graph
from math import log
from scipy.special import gamma
from scipy.special import loggamma
from random import choice
import set_of_cims as soCims
@ -19,35 +19,120 @@ import conditional_intensity_matrix as cim_class
'''
TODO: Parlare dell'idea di ciclare sulle cim senza filtrare
TODO: Parlare di gamma in scipy e math(overflow)
TODO: Problema warning overflow
TODO: Parlare del problema con gamma in scipy e math(overflow)
TODO: Problema warning overflow durante l'esecuzione
'''
class FamScoreCalculator:
"""
Has the task of calculate the FamScore of a node
Has the task of calculating the FamScore of a node
"""
def __init__(self):
np.seterr('raise')
pass
# region theta
def marginal_likelihood_theta(self,
node_id: str,
set_of_cims: soCims.SetOfCims,
graph:net_graph.NetworkGraph):
cims: cim_class.ConditionalIntensityMatrix,
alpha_xu: float = 1,
alpha_xxu: float = 1):
"""
calculate the FamScore value of the node identified by the label node_id
Parameters:
cims: np.array with all the node's cims,
alpha_xu: hyperparameter over the CTBN’s q parameters
alpha_xxu: hyperparameter over the CTBN’s theta parameters
Returns:
the value of the marginal likelihood over theta
"""
return np.sum(
[self.variable_cim_xu_marginal_likelihood_theta(cim,
alpha_xu,
alpha_xxu)
for cim in cims])
def variable_cim_xu_marginal_likelihood_theta(self,
cim: cim_class.ConditionalIntensityMatrix,
alpha_xu: float = 1,
alpha_xxu: float = 1):
"""
calculate the value of the marginal likelihood over theta of the node identified by the label node_id
calculate the value of the marginal likelihood over theta given a cim
Parameters:
node_id: the label of the node
cim: A conditional_intensity_matrix object with the sufficient statistics,
alpha_xu: hyperparameter over the CTBN’s q parameters
alpha_xxu: hyperparameter over the CTBN’s theta parameters
Returns:
the value of the marginal likelihood over theta
"""
return 2
'get cim length'
values = len(cim.state_residence_times)
print(f"transition time: {cim.state_transition_matrix}")
'compute the marginal likelihood for the current cim'
return np.sum([
self.single_cim_xu_marginal_likelihood_theta(
index,
cim,
alpha_xu,
alpha_xxu)
for index in range(values)])
def single_cim_xu_marginal_likelihood_theta(self,
index: int,
cim: cim_class.ConditionalIntensityMatrix,
alpha_xu: float = 1,
alpha_xxu: float = 1):
"""
calculate the marginal likelihood on q of the node when assumes a specif value
and a specif parents's assignment
Parameters:
index: current x instance's index
cim: A conditional_intensity_matrix object with the sufficient statistics,
alpha_xu: hyperparameter over the CTBN’s q parameters
alpha_xxu: hyperparameter over the CTBN’s theta parameters
Returns:
the marginal likelihood of the node when assumes a specif value
"""
values = list(range(len(cim.state_residence_times)))
values.remove(index)
print(values)
return (loggamma(alpha_xu) - loggamma(alpha_xu + cim.state_transition_matrix[index, index])) \
+ \
np.sum([self.single_internal_cim_xxu_marginal_likelihood_theta(
cim.state_transition_matrix[index,index_x_first],
alpha_xxu)
for index_x_first in values])
def single_internal_cim_xxu_marginal_likelihood_theta(self,
M_xxu_suff_stats: float,
alpha_xxu: float=1):
"""
calculate the second part of the marginal likelihood over theta formula
Parameters:
M_xxu_suff_stats: value of the suffucient statistic M[xx'|u]
alpha_xxu: hyperparameter over the CTBN’s theta parameters
Returns:
the marginal likelihood of the node when assumes a specif value
"""
return loggamma(alpha_xxu+M_xxu_suff_stats) - loggamma(alpha_xxu)
# endregion
# region q
def marginal_likelihood_q(self,
cims: np.array,
tau_xu:float = 1,
alpha_xu:float = 1):
tau_xu: float=1,
alpha_xu: float=1):
"""
calculate the value of the marginal likelihood over q of the node identified by the label node_id
Parameters:
@ -57,19 +142,29 @@ class FamScoreCalculator:
Returns:
the value of the marginal likelihood over q
"""
return np.prod([self.variable_cim_xu_marginal_likelihood_q(cim,tau_xu,alpha_xu) for cim in cims])
return np.sum([self.variable_cim_xu_marginal_likelihood_q(cim, tau_xu, alpha_xu) for cim in cims])
def variable_cim_xu_marginal_likelihood_q(self,
cim:cim_class.ConditionalIntensityMatrix,
tau_xu:float = 1,
alpha_xu:float = 1):
cim: cim_class.ConditionalIntensityMatrix,
tau_xu: float=1,
alpha_xu: float=1):
"""
calculate the value of the marginal likelihood over q given a cim
Parameters:
cim: A conditional_intensity_matrix object with the sufficient statistics,
tau_xu: hyperparameter over the CTBN’s q parameters
alpha_xu: hyperparameter over the CTBN’s q parameters
Returns:
the value of the marginal likelihood over q
"""
'get cim length'
values=len(cim.state_residence_times)
'compute the marginal likelihood for the current cim'
return np.prod([
return np.sum([
self.single_cim_xu_marginal_likelihood_q(
cim.state_transition_matrix[index,index],
cim.state_transition_matrix[index, index],
cim.state_residence_times[index],
tau_xu,
alpha_xu)
@ -77,31 +172,42 @@ class FamScoreCalculator:
def single_cim_xu_marginal_likelihood_q(self,
M_suff_stats:float,
T_suff_stats:float,
tau_xu:float = 1,
alpha_xu:float = 1):
M_xu_suff_stats: float,
T_xu_suff_stats: float,
tau_xu: float=1,
alpha_xu: float=1):
"""
calculate the marginal likelihood of the node when assumes a specif value
calculate the marginal likelihood on q of the node when assumes a specif value
and a specif parents's assignment
Parameters:
cims: np.array with all the node's cims,
M_xu_suff_stats: value of the suffucient statistic M[x|u]
T_xu_suff_stats: value of the suffucient statistic T[x|u]
tau_xu: hyperparameter over the CTBN’s q parameters
alpha_xu: hyperparameter over the CTBN’s q parameters
Returns:
the marginal likelihood of the node when assumes a specif value
"""
print(M_suff_stats)
return (gamma(alpha_xu + M_suff_stats + 1)* (tau_xu**(alpha_xu+1))) \
/ \
(gamma(alpha_xu + 1)*((tau_xu + T_suff_stats)**(alpha_xu + M_suff_stats + 1)))
print(f"M[x|u]: {M_xu_suff_stats} T[x|u]: {T_xu_suff_stats}")
return (
loggamma(alpha_xu + M_xu_suff_stats + 1) +
(log(tau_xu)
*
(alpha_xu+1))
) \
- \
(loggamma(alpha_xu + 1)+(
log(tau_xu + T_xu_suff_stats)
*
(alpha_xu + M_xu_suff_stats + 1))
)
# end region
def get_fam_score(self,
cims: np.array,
tau_xu:float = 1,
alpha_xu:float = 1,
alpha_xxu:float = 1):
tau_xu: float=1,
alpha_xu: float=1,
alpha_xxu: float=1):
"""
calculate the FamScore value of the node identified by the label node_id
Parameters:
@ -112,10 +218,10 @@ class FamScoreCalculator:
Returns:
the FamScore value of the node
"""
return log(
self.marginal_likelihood_q(cims,tau_xu,alpha_xu)
) \
+ \
log(
self.marginal_likelihood_theta(cims,tau_xu,alpha_xu,alpha_xxu)
)
return self.marginal_likelihood_q(cims,
tau_xu,
alpha_xu) \
+ \
self.marginal_likelihood_theta(cims,
alpha_xu,
alpha_xxu)

11
main_package/classes/network_graph.py
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@ -100,6 +100,17 @@ class NetworkGraph:
"""
self.graph.add_edges_from(list_of_edges)
def remove_edges(self, list_of_edges: typing.List):
"""
Remove the edges to the graph contained in the list list_of_edges.
Parameters:
list_of_edges
Returns:
void
"""
self.graph.remove_edges_from(list_of_edges)
def get_ordered_by_indx_set_of_parents(self, node: str):
"""
Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely

23
main_package/classes/structure_score_based_estimator.py
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@ -80,16 +80,24 @@ class StructureScoreBasedEstimator:
def estimate_parents(self,node_id:str):
"""
Use the FamScore of a node in order to find the best parent nodes
Parameters:
void
Returns:
void
"""
'Create the graph for the single node'
graph = ng.NetworkGraph(self.sample_path.structure)
'inizialize the graph for a single node'
graph.graph_struct._edges_list=[]
graph.fast_init(node_id)
#graph.graph.remove_edge('Z','X')
graph.add_edges([['Z','X']])
#graph.add_edges([['X','Z']])
graph.fast_init(node_id)
params_estimation = pe.ParametersEstimator(self.sample_path, graph)
@ -98,17 +106,16 @@ class StructureScoreBasedEstimator:
params_estimation.fast_init(node_id)
SoCims = params_estimation.compute_parameters_for_node(node_id)
'Get the node\'s parents list'
parents = graph.get_parents_by_id(node_id)
values = graph.get_states_number(parents[0])
print(f" actual_cims {len(SoCims.actual_cims)} padri {len(parents)} ")
print(f"il numero di cims è : {len(SoCims.actual_cims)}")
'calculate the FamScore for the node'
fam_score_obj = fam_score.FamScoreCalculator()
score = fam_score_obj.get_fam_score(SoCims.actual_cims)
print(f" lo score per {node_id} risulta: {score} ")
return score
'''mask = np.array([True,True])
cims = SoCims.filter_cims_with_mask(mask,[1,1])

1
main_package/tests/test_structure_score_based_estimator.py
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@ -29,7 +29,6 @@ class TestStructureScoreBasedEstimator(unittest.TestCase):
def test_esecuzione(self):
se1 = se.StructureScoreBasedEstimator(self.s1)
se1.estimate_structure()